Phd Thesis Analytical Method Development Validation
Phd Thesis Analytical Method Development Validation
Phd Thesis Bioanalytical Method Validation
The Committee recognized that the available analytical data on PCDDs, PCDFs and coplanar PCBs are limited by the lack of generally accepted criteria for intra- and inter-laboratory validation. Mutual acceptance of analytical methods would be facilitated by international collaborative studies and proficiency testing programmes. For reliable analysis of concentrations in the range of normal background contamination, laboratories must use sufficiently sensitive methods. General statistical parameters that have been established in other fields of residue analysis could be used. The requirements for acceptable analytical methods clearly need to be harmonized, so that data are comparable and can be used for risk management purposes.
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Bioanalytical Method Development And Validation Thesis …
: When applied to concentrations, variation between consumed portions of a given food group during the period considered in the analysis. For example, the within-food variation in dioxin concentration for the group ‘Fish’ would comprise the variation in dioxin concentration from one meal to the next during the period of exposure (lifetime or other) of that individual. This variation is composed of variation due to differences between species and variation related to differences between fish of the same species. The within-food variation in dioxin concentration is assumed to be equivalent to the between-sample variation for the samples considered for each food group in this analysis.
Concentrations of PCDDs, PCDFs and coplanar PCBs vary between individual products bought from retailers. This variation is indicated as the ‘within-food’ variation (see section above). Maximum limits for concentrations of contaminants in food groups are often proposed as regulatory instruments to exclude (highly) contaminated food products from the food chain. For example, Belgian legislation specifies maximum limits for PCDDs and PCDFs in poultry, beef, pork, eggs and milk (Belgisch Staatsblad, 1999).
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The hepatic sequestration of coplanar compounds markedly affects their distribution in the body. For example, whereas the liver usually contributes 10% and the adipose tissue 60% of the body burden of TCDD in mice, these fractions may increase to 67% in liver and decrease to 23% in adipose tissue in mice in which hepatic CYP proteins have been fully induced. Similar results were found in rats, clearly indicating the non-linear character of the kinetics of TCDD at concentrations that induce hepatic CYP proteins.
About 180 data sets were analysed with the power and Hill dose–response models. The power model is a limiting case of the Hill model and was used unless the Hill model provided a significantly better fit. Similarly, the standard deviation was assumed to be independent of dose, unless a contrary assumption give a significantly better fit (higher likelihood). The Hill model includes four disposable parameters, which makes it quite flexible. As a result, however, it tended to gave unreasonable (extremely low) values for some data sets. An example can be generated by applying the Hill model to the data of Theobald & Peterson (1997) on mouse epididymal weights. The epidydimal weights did not vary by dose at single doses of 15 000, 30 000 and 60 000 ng/kg bw, nor were they statistically significantly different from those of controls. Fitting of the Hill model resulted in an ED10 value of 10 value was about 30 000 ng/kg bw, which is a more biologically meaningful estimate that is more consistent with the toxicological data. Because of this feature of the Hill model, results obtained with this model were considered to be less reliable than those obtained with the power model, which has only three parameters. Consequently, the results are summarized in two ways: with both the Hill and power models as described above, and with only the power model (even if the Hill model gave a better fit).
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An example of the effect of using replacement values for undetected congeners in samples is shown in Table 11. It is clear that the method used to estimate a value for undetected congeners can dramatically affect the reported toxic equivalents value for a food. If 0 is used, low estimates of dioxin content may result, owing either to truly low concentrations in the sample or to high LODs/LOQs that did not allow quantification of the congeners with higher TEFs. For example, Table 11 shows that the imputation method has little effect on the estimated toxic equivalents for beef Stroganoff when high-resolution MS is used. However, with ion trap MS, with which the LOD is 5–10 times higher, use of 0 to equate to nondetection results in an artificially low estimate of toxic equivalents when compared with that achieved with high-resolution MS.
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When the LOQs are high relative to the decision criteria for congeners, therefore adding significantly to the estimated toxic equivalents, use of the upper-bound LOQ can result in artificially high toxic equivalents. This should be considered in defining background contamination, monitoring tolerances or estimating intake. In methods with insufficient sensitivity, the difference between lower-bound and upper-bound concentrations may be 10–100 or, in extreme cases, even higher. For example, if the sensitivity of a method is inappropriate for monitoring a tolerance, use of the concept of upper-bound LOD leads to estimates of toxic equivalents that are ‘false-positive’ results. This is a clear indication that a more sensitive method is needed. In particular, use of low-resolution MS in analysing food or samples of low weight or quantity (for a quick, easy analysis) can result in relatively high values for dioxin content as the upper-bound LOD. This bias cannot be seen in reported toxic equivalents, unless results for individual congeners are available. Thus, in defining background contamination or evaluating exposure, published data must be reviewed critically to eliminate relatively high values that are the result simply of inadequate LODs.
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The LOD or LOQ can become a critical factor in decisions based on analytical results if many congeners are not determined or if congeners representing the higher TEFs are not found. This is because the value of ‘undetected’ congeners is needed in order to estimate the overall toxic equivalents. For example, TCDD and 1,2,3,7,8-PCDD have a TEF of 1, while other, more prevalent congeners have TEFs of 0.1 or 0.01. If analysis of a sample results in detection of large quantities of TCDD or 1,2,3,7,8-PCDD, the resulting toxic equivalents will be affected by the method used to estimate the values. Some laboratories calculate the contribution of undetected congeners to toxic equivalents as zero, whereas others use the full LOD or the full LOQ to estimate toxic equivalents. Thus, estimates of dioxin content may have low or high bias. The effect of these biases is obscured by the reporting of a single value for toxic equivalents.
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